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Registro Completo |
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Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
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Data corrente: |
25/01/2024 |
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Data da última atualização: |
25/01/2024 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
GREENSTREET, L.; FAN, J.; PACHECO, F. S.; BAI, Y.; UMMUS, M. E.; DORIA, C.; BARROS, N. O.; FORSBERG, B. R.; XU, X.; FLECKER, A.; GOMES, C. |
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Afiliação: |
LAURA GREENSTREET, CORNELL UNIVERSITY; JOSHUA FAN, CORNELL UNIVERSITY; FELIPE SIQUEIRA PACHECO, CORNELL UNIVERSITY; YIWEI BAI, CORNELL UNIVERSITY; MARTA EICHEMBERGER UMMUS, CNPASA; CAROLINA DORIA, UNIVERSIDADE FEDERAL DE RONDÔNIA; NATHAN OLIVEIRA BARROS, UNIVERSIDADE FEDERAL DE JUIZ DE FORA; BRUCE R. FORSBERG, INPA; XIANGTAO XU, CORNELL UNIVERSITY; ALEXANDER FLECKER, CORNELL UNIVERSITY; CARLA GOMES, CORNELL UNIVERSITY. |
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Título: |
Detecting aquaculture with deep learning in a low-data setting. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: SIGKDD FRAGILE EARTH WORKSHOP, 2023, Long Beach. |
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Idioma: |
Inglês |
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Conteúdo: |
Aquaculture is growing rapidly in the Amazon basin and detailed spatial information is needed to understand the trade-offs between food production, economic development, and environmental impacts. Large open-source datasets of medium resolution satellite imagery offer the potential for mapping a variety of infrastructure, including aquaculture ponds. However, there are many challenges utilizing this data, including few labelled examples, class imbalance, and spatial bias. We find previous rule-based methods for mapping aquaculture perform poorly in the Amazon. By incorporating temporal information through percentile data, we show deep learning models can outperform previous methods by as much as 15% with as few as 300 labelled examples. Further, generalization to unseen regions can be improved by incorporating segmentation information through masked pooling and using contrastive pretraining to harness large quantities of unlabelled data. |
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Palavras-Chave: |
Attention; Contrastive learning; Convolutinal neural networks; Image classification; Image segmentation; Representation learning. |
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Thesagro: |
Aquicultura; Sensoriamento Remoto. |
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Thesaurus Nal: |
Aquaculture; Digital images; Neural networks; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161305/1/detecting-aquaculture-with-dee.pdf
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Marc: |
LEADER 01980nam a2200373 a 4500
001 2161305
005 2024-01-25
008 2023 bl uuuu u00u1 u #d
100 1 $aGREENSTREET, L.
245 $aDetecting aquaculture with deep learning in a low-data setting.$h[electronic resource]
260 $aIn: SIGKDD FRAGILE EARTH WORKSHOP, 2023, Long Beach.$c2023
520 $aAquaculture is growing rapidly in the Amazon basin and detailed spatial information is needed to understand the trade-offs between food production, economic development, and environmental impacts. Large open-source datasets of medium resolution satellite imagery offer the potential for mapping a variety of infrastructure, including aquaculture ponds. However, there are many challenges utilizing this data, including few labelled examples, class imbalance, and spatial bias. We find previous rule-based methods for mapping aquaculture perform poorly in the Amazon. By incorporating temporal information through percentile data, we show deep learning models can outperform previous methods by as much as 15% with as few as 300 labelled examples. Further, generalization to unseen regions can be improved by incorporating segmentation information through masked pooling and using contrastive pretraining to harness large quantities of unlabelled data.
650 $aAquaculture
650 $aDigital images
650 $aNeural networks
650 $aRemote sensing
650 $aAquicultura
650 $aSensoriamento Remoto
653 $aAttention
653 $aContrastive learning
653 $aConvolutinal neural networks
653 $aImage classification
653 $aImage segmentation
653 $aRepresentation learning
700 1 $aFAN, J.
700 1 $aPACHECO, F. S.
700 1 $aBAI, Y.
700 1 $aUMMUS, M. E.
700 1 $aDORIA, C.
700 1 $aBARROS, N. O.
700 1 $aFORSBERG, B. R.
700 1 $aXU, X.
700 1 $aFLECKER, A.
700 1 $aGOMES, C.
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Registro original: |
Embrapa Pesca e Aquicultura (CNPASA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Suínos e Aves. |
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Data corrente: |
21/12/2017 |
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Data da última atualização: |
21/12/2017 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BOLSAN, A. C.; ANTES, F. G.; CHINI, A.; ABILHÔA, H. C. Z.; TEOBALDO, M. C. S.; KUNZ, A. |
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Afiliação: |
ALICE CHIAPETTI BOLSAN, UNOESC; FABIANE GOLDSCHMIDT ANTES, CNPSA; ANGÉLICA CHINI, Universidade Estadual do Oeste do Paraná, Cascavel, PR; HÉLEN CAROLINE ZONTA ABILHÔA, Universidade Tecnológica do Paraná, Francisco Beltrão, PR; MARIA CLARA SEABRA TEOBALDO, Universidade Tecnológica do Paraná, Campo Morão, PR; AIRTON KUNZ, CNPSA. |
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Título: |
Avaliação de metodologia de análise de carbono em amostras de efluentes advindos da suinocultura. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: JORNADA DE INICIAÇÃO CIENTÍFICA, 10., 2016, Concórdia. Anais... Concórdia: Embrapa Suínos e Aves: UNC, 2017. JINC. |
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Páginas: |
113-114 |
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Idioma: |
Português |
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Palavras-Chave: |
Carbono elementar; Dejeto suíno; Sólidos. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/169654/1/final8652.pdf
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Marc: |
LEADER 00702nam a2200205 a 4500
001 2083335
005 2017-12-21
008 2017 bl uuuu u00u1 u #d
100 1 $aBOLSAN, A. C.
245 $aAvaliação de metodologia de análise de carbono em amostras de efluentes advindos da suinocultura.$h[electronic resource]
260 $aIn: JORNADA DE INICIAÇÃO CIENTÍFICA, 10., 2016, Concórdia. Anais... Concórdia: Embrapa Suínos e Aves: UNC, 2017. JINC.$c2017
300 $a113-114
653 $aCarbono elementar
653 $aDejeto suíno
653 $aSólidos
700 1 $aANTES, F. G.
700 1 $aCHINI, A.
700 1 $aABILHÔA, H. C. Z.
700 1 $aTEOBALDO, M. C. S.
700 1 $aKUNZ, A.
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Registro original: |
Embrapa Suínos e Aves (CNPSA) |
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